Previous work on fairness can be divided into three categories: pre-processing, in-processing and post-processing techniques. Pre-processing techniques, such as [1]}, [2]}, [3]}, manipulate the training data, without modifying the training procedure.
The advantage of the pre-processing techniques is that they require no knowledge of the downstream task. A basic version of our method, which uses disentanglement, but not local fairness, falls into this category. However, we further improve it to make use of labels.
Post-processing techniques intervene after a model is trained. Previous work either modify the input before it enters the model [4]}, [5]}, [6]} or modify the model's prediction [7]}, [8]}.
